Hybridizing deep learning algorithms and geostatistical approaches for improved crop yield disaggregation.

Reliable crop yield estimates at fine spatial resolution are essential for precision agriculture, food security planning, and insurance schemes. However, yield statistics are reported at coarser administrative levels, limiting their applicability for field-scale analysis. This study proposes a multi-stage hybridized framework that integrates deep learning (DL) models with geostatistical residual kriging to disaggregate village-level crop yield statistics to the pixel level. The proposed methodology is demonstrated using wheat and mustard crops as case study in the semi-arid districts, Haryana, India. The study identifies suitable data combination by evaluating multiple combinations of soil, weather, Sentinel-1, and Sentinel-2 bands data for yield disaggregation. Results show that datasets combining spectral and weather information consistently outperform other data combinations. Validation results showed that the strongest numerical accuracy was observed for machine learning algorithms, e.g., random forest, with an R2 of 0.9949, but it lacks spatial realism. On the other hand, DL models had comparable numerical accuracy and also produced smoother and more realistic spatial transitions but exhibited spatially structured residuals. To mitigate these spatial biases, residual kriging was applied to DL outputs, resulting in RMSE reduction of 35-45% and generating smoother pixel-level maps that preserved fine-scale heterogeneity and aligned with reported village yields. Moran's I analysis confirmed significant residual spatial autocorrelation for DL models, justifying the use of geostatistical correction. Thus, the proposed hybridized framework emerged as best for balancing statistical accuracy with spatially realistic yield disaggregation. This study provides one of the first empirical demonstrations of village-to-pixel yield disaggregation using the identified weather and satellite band data combination.